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Paper Citation Record · LEDGER

Unbiased Online Recurrent Optimization

As of 22 July 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1702.05043.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
1702.05043 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-25T02:05:45.602929Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-05-25T02:06:32.708142Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 5a68f9e6-382b-479e-b2fd-ea97f1b73d2e · inbound

A Unified Framework of Online Learning Algorithms for Training Recurrent Neural Networks cites this paper.

A Unified Framework of Online Learning Algorithms for Training Recurrent Neural Networks Unbiased Online Recurrent Optimization

Reference 12

Resolution
verified exact
local_arxiv, observed 2026-05-25T02:06:32.710394Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

source=pdf_text observed=2026-05-25T02:05:45.602929Z digest=sha256:9c9caa8a26f2356a9735944f7edb36e565fecb5daa270f761a016d6b90752742

Observation 804feca6-592f-4767-8c81-09dc433ac0c2 · inbound

Frame forecasting in cine MRI using the PCA respiratory motion model: comparing recurrent neural networks trained online and transformers cites this paper.

Frame forecasting in cine MRI using the PCA respiratory motion model: comparing recurrent neural networks trained online and transformers Unbiased Online Recurrent Optimization

Reference 25

Resolution
verified exact
local_arxiv, observed 2026-05-23T19:43:23.081049Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

source=pdf_text observed=2026-05-23T19:42:45.987188Z digest=sha256:470f65682cde82d44050d08f53c4f608968cbe1c2b2897fbeb08c28140dbf7fe